We have performed single unit analysis of the activity of cells located in the ventral nuclear group of thalamus in a patient with dysesthetic pain below the level of a clinically complete traumatic spinal cord transection at C5. Cells located in the parasagittal plane 14 mm lateral to the midline responded to tactile stimulation in small facial and intraoral receptive fields, which were characteristic of patients without somatosensory abnormality [30]. In this patient the 16 mm lateral parasagittal plane contained cells with receptive fields located on the occiput and neck instead of the upper extremity as would normally be expected. Cells with receptive fields on the neck and occiput had not previously been observed in recordings from single units (n = 531) responding to somatosensory stimulation [30]. Thus, on the basis of their location in a region of thalamus which normally represents parts of the body below the level of the spinal cord transection and their unusual receptive fields adjacent to these same parts of the body, we propose that the cells in the 16 mm lateral plane have lost their normal afferent input. Analysis of the autopower spectra of spike trains indicates that cells in the 16 mm lateral plane exhibited a higher mean firing rate and greater tendency to fire in bursts than cells in the 14 mm lateral plane (P less than 0.005). Finally, electrical stimulation at the recording sites in the 16 mm lateral plane evoked a burning sensation in the occiput, neck and upper extremity. These results suggest that regions of thalamus which have lost their normal somatosensory input contain neurons which exhibit abnormal spontaneous and evoked activity and that electrical stimulation of these regions can produce the sensation of burning dysesthesia.
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Sci Rep
December 2024
BAOBAB Unit, NeuroSpin center, CEA, Université Paris-Saclay, Gif-sur-Yvette, France.
Decoding states of consciousness from brain activity is a central challenge in neuroscience. Dynamic functional connectivity (dFC) allows the study of short-term temporal changes in functional connectivity (FC) between distributed brain areas. By clustering dFC matrices from resting-state fMRI, we previously described "brain patterns" that underlie different functional configurations of the brain at rest.
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December 2024
College of Electrical Engineering, Northeast Electric Power University, Jilin, 132012, China.
The scattering of tiny particles in the atmosphere causes a haze effect on remote sensing images captured by satellites and similar devices, significantly disrupting subsequent image recognition and classification. A generative adversarial network named TRPC-GAN with texture recovery and physical constraints is proposed to mitigate this impact. This network not only effectively removes haze but also better preserves the texture information of the original remote sensing image, thereby enhancing the visual quality of the dehazed image.
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December 2024
School of Electronics Engineering, Vellore Institute of Technology, Vellore, 632014, Tamilnadu, India.
A new era for diagnosing and treating Deep Vein Thrombosis (DVT) relies on precise segmentation from medical images. Our research introduces a novel algorithm, the Modified-Net architecture, which integrates a broad spectrum of architectural components tailored to detect the intricate patterns and variances in DVT imaging data. Our work integrates advanced components such as dilated convolutions for larger receptive fields, spatial pyramid pooling for context, residual and inception blocks for multiscale feature extraction, and attention mechanisms for highlighting key features.
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November 2024
Research Laboratory: Networked Objects, Control and Communication Systems, NOCCS-ENISo, National Engineering School of Sousse, University of Sousse, Soussse 4023, Tunisia.
We propose a novel architecture, Transformer Dil-DenseUNet, designed to address the challenges of accurately segmenting stroke lesions in MRI images. Precise segmentation is essential for diagnosing and treating stroke patients, as it provides critical spatial insights into the affected brain regions and the extent of damage. Traditional manual segmentation is labor-intensive and error-prone, highlighting the need for automated solutions.
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December 2024
Department of Mechanical Engineering, Boston University, Boston, MA 02215, USA.
Metamaterials hold great potential to enhance the imaging performance of magnetic resonance imaging (MRI) as auxiliary devices, due to their unique ability to confine and enhance electromagnetic fields. Despite their promise, the current implementation of metamaterials faces obstacles for practical clinical adoption due to several notable limitations, including their bulky and rigid structures, deviations from optimal resonance frequency, and inevitable interference with the radiofrequency (RF) transmission field in MRI. Herein, we address these restrictions by introducing a flexible and smart metamaterial that enhances sensitivity by conforming to patient anatomies while ensuring comfort during MRI procedures.
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